Blog
Vibe Coding Data Apps with Replit + Snowflake - Part 2 [webinar]![Vibe Coding Data Apps with Replit + Snowflake - Part 2 [webinar] — cover image](/api/site/image-proxy?v=1&u=https%3A%2F%2Fcdnimg.replit.com%2Fimages%2Fbj34pdbp%2Fmigration%2F5789f6e7af051646efeb4101bf1017aa89a4d646-1280x720.jpg%3Fw%3D3840%26q%3D80%26fit%3Dmax%26auto%3Dformat&mode=body-1200&exp=1793414400&sig=bEv3PU2dHL1UPcUStyCuySFpiJASVnj3iVq9za5d0Ss)
Published:
Feb 13, 2026The Replit Team
Summary
- Snowflake’s Vino Duraisamy and Replit’s Manny Bernabe return to turn simple dashboards into working data tools.
- Live forecasting and drilldowns explain why metrics move, not just what changed on your Snowflake dashboard.
- Custom Slack alerts fire when revenue crosses key thresholds so teams catch spikes, drops, and odd days quickly.
- An embedded AI data assistant answers plain-English questions and shows the exact SQL it ran for every query.
- Replit handles security scanning, auth, and hosting so you can ship Snowflake-powered data apps in minutes.

Introduction
In Part 1, Vino Duraisamy, Developer Advocate from Snowflake, and Replit Community Manny Bernabe, showed how to build a live dashboard in minutes. No hand-written SQL. No front-end headaches. If you missed it, check out the recap: Vibe Code Data Apps with Replit + Snowflake.
Now, in Part 2, they keep going.
No more stopping at just “pretty charts.” Manny and Vino build on the same Snowflake dataset. They add forecasting, detailed drilldowns, a Slack alert system, and an assistant that handles natural-language questions and shows the SQL behind each answer.
Manny sums it up:
“You communicate with AI using natural language, and you guide AI in terms of what you want to build rather than having to write the code yourself.”
This session covers what happens after the dashboard is live. How do you turn it into a tool your team can actually use to make decisions?
From dashboards to decision hubs
Manny gives a quick recap of the Part 1 build: a live sales dashboard powered by the Replit–Snowflake connector.
Manny describes Snowflake as an end-to-end data and AI platform for prepping, engineering, wrangling, and modeling. Replit connects through a secure connector that respects Snowflake’s role-based access control. Once it’s set up, Replit Agent can see datasets, run queries in Snowflake, and stream results into the app.

To rebuild the dashboard, Manny drops a plain-English prompt into Replit:
“I’m asking Replit to build a sales dashboard that has daily tracking that’s looking at revenue for the last sixty days.”
He adds a quick style note too, something like a dark, finance-terminal look. Agent does the rest:
- Sets up the app and file structure
- Activates the Snowflake connector with a slash command
- Generates SQL to pull and sum sales data
- Wires everything into a chart with KPI cards
In about 10–15 minutes, Agent finishes. They have a working dashboard, live on Snowflake data. No local setup. No manual charting.
This is where Part 2 really kicks off.
Forecast the future of your revenue
Seeing yesterday’s revenue is fine. Seeing where you’re headed is better.
Manny asks Replit Agent to extend the app:
“Add a forecast line that predicts revenue for the next fifteen days.”

Agent updates the project to:
- Run a time-series forecast on recent sales
- Draw a new line for projected revenue
- Show a band for the likely range
Now the dashboard answers more than just “What happened?” It also answers:
- Are we on track for this quarter’s target?
- What happens if the current trend keeps up?
If you’re technical, you can ask Agent to try different forecast models, include seasonality, or log its choices. If not, you still get a clear look at where the business is headed. No code needed.
Drill into the “why” behind every spike
A chart tells you something changed, but not why.
Manny asks Agent to add deeper drilldowns. When revenue jumps or drops, you can click through to see:
- Which regions or segments made the biggest difference
- How certain products did in the same period
- If just a few big customers changed the trend
In the webinar, Manny and Vino show examples: drilling into a spike, using filters and detail views to spot what caused it. The app shifts from static report to a surface you can explore.
All of this runs on plain-English prompts to Replit Agent. Want a new view? Just describe it and Agent writes the code and SQL.
Get ahead of changes with Slack alerts
Dashboards are only helpful if someone’s watching. But most of us aren’t staring at metrics all day.
Manny has Agent add a Slack alerting layer. The idea: ping the right channel when key numbers cross a line.

He prompts Agent to:
- Watch daily revenue and other metrics for spikes, drops, or outliers
- Set rules like “if revenue falls below X” or “if signups jump above Y”
- Post alerts to Slack with the details and a link back to the app
This makes the dashboard a lightweight monitoring tool. No more checking numbers every morning. Your team gets pinged when something actually needs attention. A sudden dip, a spike, a weird trend.
Since the app runs on Replit, you don’t need to build separate cron jobs or alerting tools. It’s all in one place.
Add an embedded AI data assistant
The last big upgrade is an assistant that sits next to the dashboard.
Manny and Vino show how to:
- Give Replit Agent safe, read-only access to Snowflake tables
- Let users ask questions about their data in plain English
- Show both the answer and the exact SQL the assistant used
For example, someone on the team can type a question like:
“What were our top five regions by revenue last quarter?”
The assistant writes the query, runs it in Snowflake, and returns both the answer and the SQL. Data teams can check the code; non-technical folks get answers fast.

Because it all runs inside a Replit app, you can keep tweaking the prompts, UI, and safeguards over time. No need to rebuild from scratch.
Ship Snowflake‑powered apps safely on Replit
All of this relies on the Snowflake connector and Replit’s secure deployment flow.
Vino highlights Snowflake’s role-based controls and how the connector respects those. Admins pick what Replit can see. Apps stay inside those limits.
On the Replit side, publishing the app triggers:
- Security scan for common vulnerabilities
- Authentication and environment variable setup
- Hosting on Replit with a shareable URL
Bottom line: you get a real data app, powered by Snowflake and Replit, with no extra plumbing.
Where vibe‑coded data apps go next
Part 2 shows what everyday analytics can look like when the tooling gets out of the way:
- Dashboards that show where you’re headed
- Drilldowns that reveal why things changed
- Alerts that tap you on the shoulder when something happens
- An assistant that makes Snowflake data easy for the whole team
If Part 1 was about getting to a working dashboard fast, Part 2 is about making that dashboard something your business can actually use.
Additional Resources
- If you want to try vibe coding your own Snowflake-powered data apps, check out the Replit–Snowflake connector and try Replit Agent with your own data.
- Set-up guide for connecting Snowflake and Replit
More
Sep 29, 2026Free the models: Harness design at the frontier
Model routers are everywhere right now, but they have a fundamental limitation. No matter if based on advanced heuristics or a small model that reads each turn and picks which LLM to use, a router will always be less capable than the model it’s choosing for. Replit Agent lets the model decide instead. The main agent, or core loop, chooses its subagents’ tier and effort, and adjusts its own as the task unfolds. Given that freedom, GPT-6 Astra hands routine implementation to less costly subagents and decides for itself where its tokens are worth spending. On both DeepSWE and Terminal-Bench, Replit Agent is Pareto-efficient against Astra on its own: no published Astra baseline costs less and scores higher. It also beats a sidekick architecture, the same setup with one long-lived worker, by 11 and 16 points. Why we scaffold less Every model release invalidates assumptions baked into the harness. As models become stronger at long-horizon tasks, they don’t need as much scaffolding at the harness layer. In practice, we’ve observed them lean more towards delegation on their own: using subagents for context management and parallelism. Recent breakthroughs, Navier–Stokes among them, came in part from coordinating swarms of agents powered by frontier models [1]. But the frontier is jagged. The strongest coding model is not necessarily the strongest at designing UIs or making slides, nor the best at writing emails. So we design our harness to let each model work its own way, with the guardrails it still needs and quality at minimum cost as the goal.
Aug 3, 2026AI adoption starts with truth
The semantic layer is the foundation AI adoption is limited by trust. A user who gets burned by a confidently wrong answer will double-check the next one, eventually routing consequential work around the system entirely. Once that happens, AI remains a tool at the edges rather than infrastructure at the center… useful, but never trusted with the workflows where its value compounds. Before a company can benefit from more capable agents, those agents need a reliable way to know what the company considers true. A semantic layer tells an agent which tables are sources of truth and how they relate. That's the floor. It is necessary, and it isn't sufficient. A semantic layer is not plumbing. It is the first act of governance for an AI-native company: the shared definitions of the business, the canonical metrics, the sources of truth, and the relationships an agent is allowed to rely on. Without it, an agent does not have a data problem. It has a language problem: several tables can each look plausible, and the model has no grounded way to know which one means "revenue," "active user," or "customer." Getting that floor right changes the shape of everything above it. A semantic layer is not the product; it is the shared contract that lets a company safely add a system of specialized capabilities instead of one generic chatbot. Once an agent can ground itself in the right entities, metrics, and relationships, it can reliably run multi-step workflows, call focused tools, retain reviewed knowledge across runs, reuse validation and analysis code, and operate through durable services where work already happens.
Jul 29, 2026Introducing Replit Design
The next era of design, for everyone